Skip to main content
Log in

An efficient and contrast-enhanced video de-hazing based on transmission estimation using HSL color model

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

This paper proposed a fast and efficient video de-hazing system with reduced computational complexity for real-time computer vision applications. Video de-hazing is an important task and extensively researched in image/video processing and computer vision. The proposed method initially developed and verified for single images and later extended for real-time video’s. The first key aspect of the proposed method is estimating the accurate transmission map using the hue, saturation, and light color model together with red, green, and blue color space. The second relevant aspect is preserving the edges and avoiding halos and artifacts by employing the median of pixels. These aspects reduce the number of computations. It does not require the most computationally complex step of refine transmission map. The advantage of this method is evaluated with five existing classical methods in terms of the average time constant (ATC), peak signal-to-noise ratio, percentage of haze improvement, average contrast of the output image, mean square error and structural similarity index. The comparative experiment shows that the proposed method is two times faster than the existing methods. The qualitative and quantitative analysis demonstrated that the proposed method can attain better de-hazing results and can be efficiently used for real-time video de-hazing applications. Based on comparative analysis, we mapped the proposed method on Raspberry Pi3 and Jetson Nano (GPU) with 24 fps (frames per second) without noticeable delay from input to output and demonstrated for the real-time video.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1956–1963 (2009)

  2. He, K., Sun, J., Tang, X.: Guided image filtering. In: European Conference on Computer Vision, Heraklion, Greece, pp. 1–14 (2010)

  3. Kratz, L., Nishino, K.: Factorizing scene Albedo and depth from a single foggy image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1701–1708 (2009)

  4. Ancuti, C.O., Ancuti, C., Hermans, C., Bekaert, P.: A fast semi-inverse approach to detect and remove the haze from a single image. In: Computer Vision ACCV 2010, Lecture Notes in Computer Science, pp. 501–514. Springer, Berlin (2010)

  5. Wang, Z., Feng, Y.: Fast single haze image enhancement. Computers and Electrical Engineering 40(3), 785–795 (2014)

    Article  Google Scholar 

  6. Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: 2013 IEEE International Conference on Computer Vision, pp. 617–624 (2013)

  7. Kim, Jin-Hwan, Jang, Won-Dong, Sim, Jae-Young, Kim, Chang-Su: Optimized contrast enhancement for real-time image and video dehazing. Journal of Visual Communication and Image Representation 24(3), 410–425 (2013)

    Article  Google Scholar 

  8. Fattal, Raanan: Dehazing using color-lines. ACM Trans. Graphics 34(1), 13 (2014)

    Article  Google Scholar 

  9. Zhu, M., He, B., Wu, Q.: Single image dehazing based on dark channel prior and energy minimization. IEEE Signal Processing Letters 25(2), 174–178 (2017)

    Article  Google Scholar 

  10. Chen, C., Do, M.N., Wang, J.: Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In: Proceedings of ECCV, pp. 576–591 (2016)

  11. Li, Z., Zheng, J.: Edge-preserving decomposition-based single image haze removal. IEEE Trans. Image Process. 24(12), 5432–5441 (2015)

    Article  MathSciNet  Google Scholar 

  12. Ju, M., Zhang, D., Wang, X.: Single image dehazing via an improved atmospheric scattering model. Vis Comput 33, 1613–1625 (2017). https://doi.org/10.1007/s00371-016-1305-1

    Article  Google Scholar 

  13. Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.H.: Single image dehazing via multi-scale convolutional neural networks. In: European Conference on Computer Vision, pp. 154–169. Springer (2016)

  14. Cai, Bolun, Xiangmin, Xu, Jia, Kui, Qing, Chunmei, Tao, Dacheng: Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing 25(11), 5187–5198 (2016)

    Article  MathSciNet  Google Scholar 

  15. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: all-in-one dehazing network. In: International Conference on Computer Vision, pp. 4770–4778 (2017)

  16. Zhu, H. et al. (2019) Single-image dehazing via compositional adversarial network, IEEE Trans. Cybern. 31:45. https://doi.org/10.1109/TCYB.2019.2955092

    Article  Google Scholar 

  17. Schechner, Y.Y., Narasimhan, S.G., Nayar, S.K.: Instant dehazing of images using polarization. In: Proceedings of IEEE Conference on Computer Vision Pattern Recognition (CVPR), vol. 1, pp. I-325-I-332 (2001)

  18. Tarel, J.-P., Hautiere, N.: fast visibility restoration from a single color or gray level image. In: IEEE International Conference on Computer Vision, Japan, pp. 2201–2208 (2009)

  19. Dana, B., Avidan, S.: Non-local image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)

  20. Xiao, C., Gan, J.: Fast image dehazing using guided joint bilateral filter. Vis Comput 28, 713–721 (2012). https://doi.org/10.1007/s00371-012-0679-y

    Article  Google Scholar 

  21. Kim, J.-H., Sim, J.-Y., Kim, C.-S.: Single image dehazing based on contrast enhancement. In: IEEE International Conference on Acoustics, p. 2011. Speech and Signal Processing (ICASSP), IEEE (2011)

  22. P. Soma, R.K. Jatoth, H. Nenavath, Fast and memory efficient de-hazing technique for real-time computer vision applications, SN Appl. Sci. 2(3), pp. 1–10 (2020)

    Article  Google Scholar 

  23. Amruta, D., Singh, S.: Development of image dehazing system. In: 2016 5th International Conference on Wireless Networks and Embedded Systems (WECON). IEEE (2016)

  24. Jiachen, Y. et al.: A real-time image dehazing method considering dark channel and statistics features. J. Real-Time Image Process. 13(3), 479–490 (2017)

    Article  Google Scholar 

  25. Preetham, A.J., Shirley, P., Smits, B.: A practical analytic model for daylight. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 91–100 (1999)

  26. Ancuti, C., Ancuti, C.O.: Christophe De Vleeschouwer D-Hazy: a dataset to evaluate quantitatively dehazing algorithms. In: IEEE International Conference on Image Processing (ICIP) ICIP’16 2016 Pheonix, USA

  27. Zhang, K., Wang, S., Zhang, X.: New metric for quality assessment of digital images based on weighted mean square error. International Society for Optics and Photonics, City (2002)

  28. Z. Wang, A.C. Bovik, H.R. Sheik, E.P. Simoncelli, Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process. 13(4), 1–14 (2004). DOI: 10.1109/TIP.2003.819861

    Article  Google Scholar 

  29. Matt, R, Wallace, S: Getting started with raspberry PI. O’Reilly Media, Inc., Newton (2012)

    Google Scholar 

  30. Umesh, P.: Image Processing in Python, CSI Communications (2012)

  31. Jetson Nano.https://developer.nvidia.com/embedded/jetson-nano-developer-kit. Accessed 8 Nov 2020

  32. Cass, S.: Nvidia makes it easy to embed AI: The Jetson nano packs a lot of machine-learning power into DIY projects-[Hands on]. IEEE Spectrum. 57(7), 14–6 (2020 Jun 25)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by Science and Engineering Research Board (SERB) India, under the Grant of EEQ/2016/000556

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Prathap Soma.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary material 1 (mp4 76017 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soma, P., Jatoth, R.K. An efficient and contrast-enhanced video de-hazing based on transmission estimation using HSL color model. Vis Comput 38, 2569–2580 (2022). https://doi.org/10.1007/s00371-021-02132-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-021-02132-3

Keywords

Navigation